We all know that while reading a book, indexes are very important. When we try to search for a topic in the book, we first roll our eyes through the index page. If the index is found, then we go to the specific page number for that topic. But there is a drawback here. We are using additional pages for the sake of this indexing. Similarly, MongoDB needs to go through all the documents whenever we query for something. If the document stores indexes for important fields, it can give back data to us quickly. At the same time, we are wasting extra space for indexing.
In the computing field, the B-tree is an important data structure to implement indexing because it can categorize nodes. By traversing that tree, we can find the data we need in fewer steps. We can create an index using the createIndex function provided by MongoDB. Let us take an example of students and their scores in an examination. We will be doing GET operations more frequently with...